3D retinal disruptions detection using optical coherence tomography

ABSTRACT

System and method for 3D retinal disruption/elevation detection, measurement and presentation using Optical Coherence Tomography (OCT) are provided. The present invention is capable of detecting and measuring the abnormal changes of retinal layers (retinal disruptions), caused by retinal diseases, such as hard drusen, soft drusen, Pigment Epithelium Detachment (PED), Choroidal Neovascularization (CNV), Geographic Atrophy (GA), intra retinal fluid space, and exudates etc. The presentations of the results are provided with quantitative measurements of disruptions in retina and can be used for diagnosis and treatment of retinal diseases.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application61/414,805, filed on Nov. 17, 2010, which is herein incorporated byreference in its entirety.

BACKGROUND

1. Field of Invention

The embodiments described herein relates to methods and systems todetect and measure the retinal disruption/elevation of optical coherencetomography (OCT) data, as well as to present the detection andmeasurement results using 3D OCT data.

2. Background State of the Arts

Optical Coherence Tomography (OCT) has been an important modality forimaging eyes and facilitating ophthalmologists to diagnose and treatsubjects with different eye diseases, retinal diseases in particular.The importance of OCT to the field of ophthalmology has alsodramatically increased since Fourier Domain OCT (FD-OCT) becamecommercially available. FD-OCT has much higher scanning speed and higherresolution than the traditional Time Domain OCT (TD-OCT) technologies.

One of the major pathologic changes for retinal subjects is retinallayers disruption from their normal locations, especially around theRetinal Pigment Epithelium (RPE) and Photoreceptor Inner Segment/OuterSegment (PR-IS/OS) area. Quantitative measurements of such disruptionsprovide important information for ophthalmologists to diagnose and treatpatients.

Previous methods using 3D OCT data follow the same scheme of firstsegmenting retinal layers, and then detecting the disruption (e.g.drusen) by comparing the segmented layers with expected referencedlayers or with some layers which are elevated from the segmented layersby some constants. The referenced layers are often generated by fittingsome smooth surfaces to the segmented layers, assuming the layers arenot disrupted by any disease or pathology. In general, the presence of adisruption is determined by only comparing two 2D surfaces; this meansthe original 3D OCT data is not fully utilized after the layersegmentations have been performed. Such scheme has at least four majordrawbacks. First, such detection methods are error prone because theyare highly dependent on results of layer segmentations. If the 2Dsurface segmentation is not optimal, the disruption detection will bedirectly affected and likely produce inaccurate results. Second, toreduce noise effects associated with OCT data, layer segmentation oftenemploys smoothing operation which can likely introduce the problem ofscale. Excessive smoothing (such as the case with a large smoothingscale) will likely reduce details in desired features, whileinsufficient smoothing (with a small smoothing scale) will likely beinadequate to reduce noise effectively to generate optimal layersegmentations. Third, methods assuming constant elevations fromsegmented layers are less clinically meaningful because disruptionsoften occur locally with different and unpredictable sizes. Finally, amajority of existing methods only detect disruptions above thereferenced layers, and any disruptions under the referenced layers areignored. Since disruptions can occur above and below the referencelayers of interest, it is important to devise a method to detect andmeasure disruptions in both scenarios.

SUMMARY

This Summary is provided to briefly indicate the nature and substance ofthe invention. It is submitted with the understanding that it will notbe used to interpret or limit the scope or meaning of the claims.

In accordance with some embodiments of the present invention, an imagingapparatus includes an optical source, an x-y scanner receiving lightfrom the optical source and directing it onto a sample, a detectorreceiving reflected light from the scanner, and a computer receiving asignal from the detector and providing a 3D data set containing voxelsof a sample, the computer further executing instructions for processingthe 3D data set, identifying one or more 3D seeds from the 3D data set,performing image processing to obtain characteristics of 3D disruptionsfrom the 3D seeds, generating measurements for the 3D disruptions, anddisplaying the results of the 3D disruptions.

An image processing method according to some embodiments of the presentinvention includes acquiring a 3D data set utilizing an OCT system,processing the 3D data set, identifying one or more 3D seeds from the 3Ddata set, performing image processing to obtain characteristics of 3Ddisruptions utilizing the 3D seeds, generating measurements for the 3Ddisruptions, and displaying the results of the 3D disruptions.

These and other embodiments are further discussed below with respect tothe following figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary flowchart of some embodiments of the presentinvention.

FIG. 2 is an example of a clinically useful presentation scheme for adisruption report.

FIG. 3 shows an example of a color fundus photograph with drusen.

FIG. 4 shows examples of retinal disruptions in en face images withdifferent offsets in the z directions.

FIG. 5 is an example of an Intelligent Fundus Image (IFI) of someembodiments of the present invention.

FIG. 6 is an exemplary presentation scheme for a progression report.

FIG. 7 is a schematic illustration of an Optical Coherence Tomography(OCT) scanner.

FIG. 8 a-d show example images used for 3D disruption seeds generation:(a) Adaptive Seed Search Image (AS SI) using elevation map between innersegment and outer segment (IS/OS) and Retinal Pigment Epithelium (RPE)fit used for 3D disruption seeds generation (b) ASSI to generatedrusen/Pigment Epithelium Detachment (PED) 3D disruption seeds; (c) ASSIto generate geographic atrophy 3D disruption seeds; and (d) example 3Ddisruption seeds for Geographic Atrophy (GA) detection.

FIG. 9 a-d show exemplary images of disruption region: (a) beforedisruption post processing; (b) after disruption post processing; (c)user defined sensitivity at 0.5; and (d) user defined sensitivity at1.0.

FIG. 10 is an exemplary plot of the relationship between the size of thedisruption and its distance to the center of the fovea.

FIG. 11 shows exemplary displays of 3D disruption clouds (a) at RPE-fitlayer, and (b) at RPE layer.

Where appropriate, elements having the same or similar functions havethe same element designation. Figures are not to scale and do notillustrate relative sizes.

DETAILED DESCRIPTION

The aspects and embodiments of the invention disclosed herein relate toa computer-aided detection and measurement system and method to detectand measure the retinal disruption (or elevation), as well as to presentthe results using the OCT 3D volume data.

In some embodiments, the retinal disruptions are detected by 3D regiongrowing methods with different constraints, such as 3D shape, size,intensity and texture, 3D labeled mask generation, and IntelligentFundus Image (IFI) construction.

In some embodiments, the 3D disruption seeds for the 3D region growingare obtained by a method which contains retinal layer segmentation ofInner Limiting Membrane (ILM), Inner Plexiform Layer (IPL), OuterPlexiform Layer (OPL), Photoreceptor Inner Segment/Outer Segment(IS/OS), and Retinal Pigment Epithelium (RPE) layers, normal RPE layerconstruction, IS/OS elevation map construction, Adaptive Seed SearchImage (ASSI) construction, and 3D disruption seeds detection from all ofthe above information as well as the OCT 3D volume data.

In some embodiments, based on the detected disruption, the followingquantitative measurements are performed: the number of disruptions, the3D boundary of each disruption, the size (diameter, area and volume) ofeach disruption, the distribution of the disruptions, the sum of thedisruptions in size in defined regions, e.g. the standard ETDRS (EarlyTreatment Diabetic Retinopathy Study) sectors, and the change of thesemeasurements over time.

In some embodiments, an interactive Graphical User Interface (GUI) isprovided to display the above measurements as well as for changing theintermediate results, e.g. segmented retinal layers, disruptions seeds,and disruption boundaries in 3D, to correct some errors from theauto-method to obtain more accurate results. In some embodiments,progression analysis is performed and the report is provided whenmultiple datasets are available.

The approaches discussed herein can be implemented on a device formeasuring samples using optical coherence tomography (OCT). One suchdevice has been commercially available in the US and internationally bythe assignee herein under the trademark RTVue®. A more compact versionof such device has also been commercially available in the US andinternationally by the assignee herein under the trademark iVue®. Boththe RTVue® and the iVue® are frequency domain OCT system with abroadband light source and a spectrometer, and acquire OCT data setswith both high definition scan(s) and lower resolution data cubes withina short period of time capable of clinical interpretation and diagnosisby clinicians. Embodiments described in this disclosure can be appliedto any imaging devices and are not limited to the OCT technologydiscussed above.

A system and method for retinal disruption detection, measurement andpresentation using a 3D data set acquired using an OCT scanner isdisclosed. Embodiments of the present invention can be utilized tofacilitate diagnosis and treatment of retinal diseases with quantitativemeasurements of disruptions in retina.

FIG. 7 illustrates an example of an OCT imager 700 that can be utilizedin 3D retinal disruption detection according to some embodiments of thepresent invention. OCT imager 700 includes light source 701 supplyinglight to coupler 703, which directs the light through the sampling armto XY scan 704 and through the reference arm to optical delay 705. XYscan 704 scans the light across eye 709 and collects the reflected lightfrom eye 709. Light reflected from eye 709 is captured in XY scan 704and combined with light reflected from optical delay 705 in coupler 703to generate an interference signal. The interference signal is coupledinto detector 702. OCT imager 700 can be a time domain OCT imager, inwhich case depth (or A-scans) are obtained by scanning optical delay705, or a Fourier domain imager, in which case detector 702 is aspectrometer that captures the interference signal as a function ofwavelength. In either case, the OCT A-scans are captured by computer708. Collections of A-scans taken along an XY pattern are utilized incomputer 708 to generate 3-D OCT data sets. Computer 708 can also beutilized to process the 3-D OCT data sets into 2-D images according tosome embodiments of the present invention. Computer 708 can be anydevice capable of processing data and may include any number ofprocessors or microcontrollers with associated data storage such asmemory or fixed storage media and supporting circuitry.

In further illustration, FIG. 1 is an exemplary flowchart of someembodiments of the present invention. In the flowchart illustrated inFIG. 1, there are ten steps of the present retinal disruption detection,measurement and presentation method, namely, 1) 3D disruption seedsdetection 100, including five sub-steps 101-105; 2) 3D region growing106; 3) 3D labeled mask generation 107; 4) Intelligent Fundus Image(IFI) construction 108; 5) disruption region post-processing 109; 6)optional disruption sensitivity calculation 110; 7) measurementsassessment 111; 8) optional interactive presentation 112; 9) finalpresentation and report 113, and 10) optional progression analysis 114.

3D Disruption Seeds Detection

The first step 100 of the flowchart in FIG. 1 is to facilitate thedetection of 3D seeds of potential disruptions in the retina, whereindisruptions seeds are detected and identified for subsequent 3D regiongrowing to determine a final 3D disruption volume in the 3D data set. Asshown in FIG. 1, step 100 includes five sub-steps for preparing andidentifying the seeds and they are described in detailed below.

In sub-step 101, segmentation for different retinal layers of a subjecteye, such as Inner Limiting Membrane (ILM), Inner Plexiform Layer (IPL),Outer Plexiform Layer (OPL), Photoreceptor Inner Segment/Outer Segment(PR-IS/OS), Retinal Pigment Epithelium (RPE), choroid boundaries, orother layers of interest, can be performed. Layer segmentation iscommonly performed on measurement data acquired using the OCT technologyand numerous methods have been used to achieve retinal layersegmentation in OCT data set. Some well-known methods are graph-cut,active contour model (snake), level set theory, and dynamic programming(see for example, J. A. Noble et. al., “Ultrasound Image Segmentation: ASurvey”, IEEE Transactions on Medical Imaging, vol. 25, no. 8, pp.987-1010, 2006).

The second sub-step 102 is to determine a segmentation curve or surfaceas a representation of a normal retinal layer, e.g., RPE layer, locationby assuming the layer is not disrupted by any disease or pathology;hence, points of disruption suspects on the layer are not considered.For example, this representation of a normal RPE layer location iscalled a “RPE-fit”. In some embodiments, a RPE-fit is assumed to beconvex and smooth in local segments and free of disruptions. Afterexcluding the suspected pathological segments, the RPE-fit can be fittedby low order polynomials to represent a normal RPE layer. For instance,second order or third order polynomials are sufficient to achieve anideal RPE-fit layer. The idea of fitting the RPE surface to the actualRPE from SD-OCT image volume was presented soon after the SD-OCT wasdeveloped and methods for line or surface fitting are well-known in theart (see for example, M. Szkulmowski et. Al, “Analysis of posteriorretinal layers in spectral optical coherence tomography images of thenormal retina and retinal pathologies”, Journal of Biomedical Optics12(4), 2007).

The third sub-step 103 is to create an elevation map from the layers ofinterest. For example, an elevation map of the IS/OS can be created tohelp identify seed candidates needed for subsequent 3D retinaldisruption detection. In some embodiments, the IS/OS segmented layerfrom step 101 is compared to the RPE-fit generated in step 102 to createan elevation map of IS/OS. FIG. 8 a shows an example of an elevation mapbetween IS/OS and RPE-fit that can be used to detect the 3D disruptionseeds for drusens with further processing step in accordance with someembodiments of this invention.

The fourth sub-step 104 is to identify seed candidates by evaluating theOCT data distribution for each A-scan between the IS/OS segmented layerstep 101 and the RPE-fit layer step 102, or below the RPE-fit layer,depending on the type of disruptions. The evaluation of each A-scanhelps confine the search range for the seed candidates and results in animage, called an “Adaptive Seed Search Image” (AS SI). The ASSI enhancesthe bright regions in the image for seed candidates that can be used toidentify potential locations of retina disruptions with furtherprocessing.

In some embodiments, sub-step 104 can be performed without performingsub-step 103. For example, to detect GA, the 3D disruption seeds can bedetected directly from an ASSI constructed below the RPE-fit layer. Theelevation map constructed between IS/OS to RPE fit in step 103 can laterbe used to further refine the 3D disruption detection constraints andthe GA detection results.

FIG. 8 b is an example ASSI by performing further image processing, suchas edge detection, on an en face image to enhance the disruption regions810 used in the seeds detection step 105. FIG. 8 c is another exampleASSI generated by performing further image processing, such as commonlyknown smoothing filter(s), on an en face image to enhance the disruptionregions 820 used to detect a seed in the case of GA disruption. FIG. 8 dis an exemplary 2D disruption seeds image generated by locating theregions with value higher than a selected threshold in FIG. 8 c.

In some embodiments, local intensity maxima can be extracted from theIS/OS elevation map and the x-y positions of these local maxima can thenbe considered as the x-y positions of the seed candidates of retinaldisruptions for further 3D seed detection in step 105. The localintensity maxima can be detected using commonly used 2D imagesegmentation algorithm, such as, Otsu adaptive thresholding (Otsu, N, “athreshold selection method from gray-level histograms”, IEEETransactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66,1979), a marker controlled watershed transform (Vincent, L and Pierre,S, “Watersheds in Digital Spaces: an efficient algorithm based onimmersion simulations”, IEEE Transactions on Pattern Analysis andMachine Intelligence, Vol. 13, No. 6, pp. 583-598, 1991.), or a kernelbased mean shift clustering (Cheng, Yizong, “Mean shift, mode seekingand clustering”, IEEE Transactions on Pattern Analysis and MachineIntelligence, Vol. 17, No. 8, 1995). In some embodiments, the resultinglocal intensity maxima (brightest points) can be extracted from the ASSIobtained in step 104 and their respective x-y positions of these localmaxima can be selected as the 2D seed candidates of 3D retinaldisruptions segmentation.

The last sub-step 105 of step 100 is to detect 3D disruption seeds byincorporating the 3D OCT data set, the 2D seed candidates from theelevation map of IS/OS map construction step 103, and/or the 2D seedcandidates from the ASSI construction step 104. The previous stepsdemonstrate the steps to detect the x-y positions of the seed candidatesaccording to an embodiment in the exemplary flowchart in FIG. 1. Toobtain seed candidates in a 3D data set, the z positions of the seedcandidates need to be determined, for example, by searching the seedcandidates from steps 103 and 104 along the A-scan direction with thex-y positions identified in steps 103 and 104. In some embodiments, 3Dvoxels with the most expected intensity values can be selected to be the3D seeds. Other common known methods for a person of ordinary skills inthe art can be used to identify the z positions using the x-y positionsobtained from previous steps. This method effectively determines the x,y and z locations of the 3D seeds to facilitate 3D retinal disruptiondetection.

FIG. 4 shows examples of retinal disruptions in en face images withdifferent offsets in the z directions. Different techniques have beenused to generate en face images (see for example U.S. application Ser.No. 12/909,648). Image 410 shows an en face image obtained from theentire volume of a 3D data set. Image 420 shows an image with 55 umoffset from the RPE-fit. Drusens 425 are indicated as the bright spotsor brighter boundary surrounding darker area in the image 420. Image 430is an image from the same 3D data set used to generate image 410 with 54um offset from the RPE-fit. Drusens 435 are again the bright spots inthe image 430. Image 440 is another image using the same 3D data setwith 74 um offset from the RPE-fit, with drusens 445 as the bring spots.The characteristics of the drusens 425, 435 and 445 changes from images420, 430 and 440, with different offset amounts from the RPE-fit. Usingthis simple approach as shown in FIG. 4, the detection of the actualdrusens is likely to be error prone and non-reproducible because thecharacteristics of drusens in the images are highly dependent on theamount of offsets as shown herein. Therefore, 3D segmentation methodsused for 3D retinal disruption detection can significantly improveclinical usefulness of the OCT data.

The embodiments disclosed herein do not simply use 2D segmented surfacesto identify the disruptions directly, but utilize the 2D segmentedsurfaces as intermediate results to identify local 3D seeds. Based onthe local 3D seeds from determined in step 105, 3D region growingtechniques can then be applied to detect areas of interest morereliably, especially disruptions of various sizes and locations.Therefore, the accuracy of 2D surface segmentation is less critical ascompared to some current methods discussed above.

3D Region Growing

In the exemplary flowchart illustrated in FIG. 1, the next step, step106, is to perform 3D region growing in the 3D OCT data set.Segmentation methods using 2D region growing are well known in the field(see for example, Adam and Bischof, “Seeded Region Growing,” IEEETransactions on Pattern Analysis and Machine Intelligence, vol. 16, pp.641-647, 1994). 3D region growing in step 106 can be performed using the3D seeds identified in step 105 with region growing constraints. Someexamples of region growing constraints are 3D shape and size determinedby clinical understanding of pathologies and image properties of the OCTdata set. For instance, many of the retinal disruptions/pathologies,such as, hard and soft drusen, PED, and CNV, form a half-spherical shapeabove a normal RPE location (RPE-fit) in most cases. Also, imagecharacteristics of the OCT data set, such as area of high reflectivityand salt-pepper texture features, are commonly observed in hard drusenformation. A 3D region growing method utilizing these constraintsimproves the robustness and accuracy of the region growing process andsubstantially reduces some commonly known leaking problem oftenencountered in region growing techniques. In some embodiments, 3D regiongrowing step 106 utilizing the above mentioned constraints using the 3Dseeds identified in step 105 is capable of detecting disruptions withdiameter as small as 60 um.

Alternatively, in situations where processing time is critical, the 3Ddisruption regions can be detected using other 3D segmentationtechniques other than the 3D region growing method. In some embodiments,3D segmentation can be performed by interpolating multiple 2Dcross-sectional regions at different z distance based on the 3D seedsidentified in step 105, similar to piecing together volume using contourmap. Some commonly used interpolation methods, such as, linearly,bi-linear, b-spline interpolation can be employed. In some otherembodiments, to further reduce process time and computation burden,enhanced 2D retinal disruption can be obtained using the ASSI. Forexample, ASSI can be constructed from a certain clinically meaningfulpre-selected range of z positions, in addition to the adaptive localseed identifications discussed in steps 101 to 105. A 2D regionsegmentation algorithm (for example, marker-controlled watershedsegmentation) can then be applied to generate a 2D counterpart of the 3Dlabeled disruption mask as in step 107. In some embodiments, for drusenand PED type of disruption, the ASSI can be constructed from 100 um to30 um above the RPE-fit layer. For Geographic Atrophy (GA) type ofdisruption, the ASSI can be constructed from 100 um to 400 um below theRPE-fit layer. Additional ASSI image can also be constructed from 100 umabove IS/OS layer to IS/OS layer as reference of reflectivity.

3D Labeled Mask Generation

The next step 107 is to label voxels of each of the 3D connected objectssegmented using the 3D region growing step 106. For example, thelabeling procedure can be as discussed below. First, all voxels filledby the 3D region growing step 106 can be initially assigned a mask valueN larger than the maximum allowable number of 3D connected objectsidentified in step 106 (e.g., N=255, assuming there is less than 255 3Dconnected objects in the 3D data set); while voxels not filled in step106 are assigned values of 0. Next, an iterative process can be used tolabel each of the 3D connected objects with a unique value. Theiterative process can have k iterative steps, where 1≦k≦K, and K is thetotal number of 3D connected objects from step 106. A commonly usedflood-fill algorithm can also be applied to a randomly selected voxelhaving a mask value N to search for all mask voxels connected to thisselected voxel. New mask values k will be assigned to these connectedvoxels having mask values N previously. After this first iteration, kwill be incremented (e.g. k=k+1) and the flood-fill algorithm will beapplied to another randomly selected voxel having a mask value N tosearch for voxels connected to this randomly selected voxel. The maskvalues of these voxels will then be updated to the incremented k value.This iteration process continues and stops at the K^(th) iteration,where there is no more voxel with the mask value initially assigned maskvalue N. This process labels the K total number of 3D connected objectsin the 3D data set to facilitate further processing steps 108-110 and toevaluate subsequent disruption measurements in step 111.

Intelligent Fundus Image (IFI) Construction

Based on the 3D labeled mask inform step 107, an Intelligent FundusImage (IFI) can be constructed in step 108. 3D data smoothing andenhancement techniques such as nonlinear filtering and histogramequalization can be applied, depending on the application at hand, toone or more specific 3D connected objects to generate the optimal fundusimage representation. An example of such IFI is shown in FIG. 5. The IFIimage as shown in FIG. 5 resolves the problem of the sensitivity ofdifferent arbitrary offset amounts illustrated in FIG. 4. The IFI imageas a result of the process described above produces constant and moreaccurate retinal disruptions characteristics. Image 510 is an imagegenerated from the same 3D data set used for image 410. Thecharacteristics of drusens 520 are more pronounced and can be moreclearly identified than drusens 425, 435, or 445 illustrated in images420, 430, and 440, respectively, of FIG. 4.

Disruption Region Post-Processing

In some embodiments, disruption region post-processing step 109 can beapplied to the disruption labeled mask to remove or minimize the impactsof motion artifacts, such as eye movement during the data acquisition,false positive regions near disc area or due to size and shape, oroptical and electronic noise. For example, eye motion can be detectedusing edge information and histogram distribution of the IFI image. Amotion probability map can be constructed and a probability of motionartifacts can then be assigned to each labeled region so that regionswith higher motion probability are removed from the disruption image.FIGS. 9 a-b show examples of disruption regions before (FIG. 9 a) andafter (FIG. 9 b) the post processing. In FIG. 9 b, the artifacts due toeye movement, too small and too slim clusters, and false positiveclusters associated with the optic disc area in FIG. 9 a were removed.In some embodiments, instead of removing the motion and other artifactsregion, the artifacts could be displayed as different shades or colorsto make them more distinguishable for easier interpretation.

Disruption Sensitivity Calculation

In accordance with some embodiments of the present invention, anoptional step 110 can be implemented to enhance the retinal disruptiondetection results; a disruption sensitivity metric can be defined andassigned to each disruption region or volume. A sensitivity value can beset in a range reasonable to the user, such as a range of [0.0, 1.0] or[0%, 100%]. User can adjust this parameter to display disruption regionor volume at a desire sensitivity level. A high sensitivity value canlead to a larger number of disruptions with smaller sizes to bedetected, and vice versa. Alternatively, a user defined fixedsensitivity value can be selected by default. The sensitivity value canbe assigned, manually or automatically, using information from the 3DOCT data set, such as, the height of the elevation, the area of thedisruption, the volume of the disruption, or combination of thesemetrics. For instance, if the user wants to visualize large drusens orPED, a lower sensitivity value, such as 0.5 out of 1.0, can be selected.On the contrary, if the user wants to visualize all possibledisruptions, a sensitivity value of 1.0 out of 1.0 could be selected.FIG. 9 c-d show examples of detected disruption regions with differentsensitivity values (sensitivity value at 0.5 and 1.0, respectively).

Disruption Measurements

3D regions of retinal disruptions detection can be achieved as describedin steps 100-110 described above. The next step, step 111 of FIG. 1, isto obtain quantified measurements and perform image processing toidentify characteristics and properties of the disruptions in a mannerto aid in the diagnosis of a disease or a disease state. Themeasurements of the 3D retinal disruptions are clinically meaningful;and the image processing helps the user to evaluate the identifiedconditions in a clinically useful manner. In some embodiments of thepresent invention, the following measurements and image processing canbe performed in step 111. Such measurements may include but are notlimited to the following:

-   -   The number of disruptions;    -   The 3D boundary of each disruption;    -   The size (diameter) of each disruption;    -   The area of each disruption;    -   The volume of each disruption;    -   The distribution of the size in the categories of small (<63        um), intermediate (63 um˜124 um), large (125 um˜249 um), very        large (250 um˜499 um), super large (>=500 um);    -   The distribution of the volume, in the same size categories        defined above;    -   The total size of the disruptions in any user specified area;    -   The total volume of the disruptions in any user specified area;    -   The total size of the disruptions in the standard ETDRS (Early        Treatment Diabetic Retinopathy Study) sectors;    -   Total circle (6 mm diameter circle);    -   Center circle (1 mm diameter circle);    -   Inner ring (1 mm˜3 mm ring);    -   Out ring (3 mm˜6 mm ring);    -   Inner Tempo;    -   Inner Superior;    -   Inner Nasal;    -   Inner Inferior;    -   Out Tempo;    -   Outer Superior;    -   Outer Nasal;    -   Outer Inferior;    -   The total volume of the disruptions in the standard ETDRS (Early        Treatment Diabetic Retinopathy Study) sectors as defined above;        and    -   Disruption area percentage in the standard ETDRS (Early        Treatment Diabetic Retinopathy Study) sectors as defined above.

In addition to calculating these numerical measurements, they can bedisplayed in a clinically meaningful manner. For example, distances ofdisruption centers to a reference point, such as the fovea, can becalculated and plotted in relation to some measurements, such asdiameter, area, or volume of the disruptions. FIG. 10 shows an exemplaryplot of the size of the disruption versus its distance to fovea.

Interactive Presentation

An intuitive and user-friendly Graphical User Interface (GUI) providinguser interaction with the 3D disruption regions can be employed in someembodiments of the present invention. An interactive GUI presentationstep 112 of the 3D retinal disruption segmentation steps 106 to 110 andthe measurements performed in step 111 can be incorporated in theexemplary flowchart in FIG. 1. An example of an intuitive anduser-friendly user interaction could allow a user to modify the layersegmentation results form step 101 to better identify the region ofinterest. Another example of such user interaction can allow the user tomodify, add, or remove 3D disruption seeds inform step 105 forcustomized results. Also, such user interaction can allow the user tomodify, add, remove, or emphasize the resulting 3D disruptionsegmentation from step 106. The interactive presentation and GUI allowthe user to verify and confirm the detection results to better meet theuser's clinical needs. From the retinal layer segmentation to thegeneration of measurements and image processing of the detected 3Ddisruption from steps 100-111 can be evaluated again when the userchooses to make a modification through the interactive GUI presentationstep 112.

Final Presentation and Report

After the user has verified and confirmed the 3D retinal disruptionresults and measurements, a clinically useful detection report can begenerated in step 113. FIG. 2 illustrates an example of a clinicallyuseful presentation scheme for a disruption results report. Two IFI 200and 202 are displayed in this sample report. IFI 200 is an IFI with aETDRS grid overlay with the detected retinal disruptions highlighted indifferent colors. A color scheme is used in some embodiments to helpeasily identify characteristics of the disruptions. Some clinicallyuseful characteristics are area, volume and depth. In this instance inFIG. 2, the color scheme of green 203, purple 204 and red 205 are usedto designate the size of the disruption for intermediate, large and verylarge size, respectively. IFI 202 is an IFI with another ETDRS gridoverlay with an area percentage indicated in each of the ETDRS gridregion. IFI 202 is a quantitative display to indicate the percentage ofarea affected in each of the ETDRS region. It is clinically useful to beable to identify the location and concentration of disruption in each ofthe sensitive ETDRS region. Region 206 is one ETDRS region where 1% inarea of the total number of drusens is located. Image 207 is one exampleof B-scan image where retinal layers are segmented and displayed (firstred: ILM, green: IPL, cyan: RPE, second red: RPE-fit). Region 208 is thedisruption regions (pink bumps) detected for the corresponding B-scanimage. Additionally, some commonly used clinical information can bedisplayed or integrated in such a report, namely, total disruptionnumber, disruption number for each size category (small, intermediate,large, very large, and super large), total disruption area, disruptionarea for each ETDRS sectors, total disruption volume, disruption volumefor each ETDRS sectors, disruption area percentage for each ETDRSsectors, color ETDRS region by the disruption area percentage, overlaythe above colored ETDRS region onto the IFI image 202, overlay the abovecolored ETDRS region onto the color fundus photograph, and mark thedisruption boundaries on to in image, with different color for theboundary to distinguish the size category 200. FIG. 3 shows an examplecolor fundus photograph commonly used by clinicians to identify one formof retinal disruptions (drusens). In photograph 300, drusens 302 aredisplayed in yellowish color. However, this traditional presentation orreport is less clinically useful than the quantitative, objective andmore accurate 3D retinal disruption detection method and presentationdiscussed above.

Alternatively, the final presentation of the 3D disruptions can berendered as “3D disruption clouds” in a 3D display interface. The 3D OCTscan could be displayed in full or at certain layers depending on theuser's selection. The 3D disruption clouds can be rendered as pseudo orsemi-transparent color volume to present of 3D location and shape. FIG.11 a shows an exemplary 3D disruption clouds display at the RPE-fitlayer, and FIG. 11 b shows another example at the RPE layer.

Progression Analysis

In addition to a single clinically useful final report, it is often moreadvantageous for a clinician to be able to monitor and effectively tracka condition or a disease state in the field of ophthalmology. Someembodiments of the present invention include an integrated progressionreport generated in reporting step 114, which compares and presentsclinically useful information from longitudinal exams acquired within oracross multiple visits. FIG. 6 is an exemplary presentation scheme for aprogression report; it shows an example of a progression analysis fortwo data sets acquired in two different points in time, points 600 and610 (retinal disruption 620 segmented with boundaries). The number oflongitudinal data sets can be extended from two to a reasonable numberthat would be clinically meaningful. When multiple datasets areavailable (often from multiple patient visits), progression analysis canbe performed and the progression report can then be generated. Whenmultiple data sets are available, the analysis from steps 100 to 113 ofdisruption 3D seeds detection will be adapted accordingly to use themultiple datasets. Using multiple dataset can further help validate andenhance robustness of the 3D disruption detection method, especiallywhen the disease state does not change significantly between visits.Some common and clinically meaningful display and representation ofmeasurements can be used in the progression report, for example, changein total disruption number, change in disruption number for each sizecategory, change in disruption area, change in disruption area for eachETDRS sectors, change in disruption volume, change in disruption areapercentage for each ETDRS sectors, and change in the disruption region,overlaid onto IFI images, with different colors for the disruption areasonly in one data, and disruption areas in both datasets. Examples ofsome of these useful information are shown in graphs 630 and 640. Asimilar report can be created for other disruption presentation andmeasurement discussed above by a person with ordinary skill in the artwithin the scope of the present invention.

As is demonstrated from the analysis of data obtained from each of theprior art techniques, none of them provide a complete, reliable, oraccurate analysis of the 3D retinal disruption. Each fails to reliablydetermine one or more measurement or segmentation in an accurate andreproducible manner.

The above examples are provided in order to demonstrate and furtherillustrate certain embodiments and aspects of the present invention andare not to be construed as limiting the scope thereof. In thedescription above, reference is made primarily to the eye as the object.This has to be understood as merely a way to help the description andnot as a restriction of the application of the present invention. Assuch, where the term “eye” is used, a more general transparent andscattering object or organ may be sought instead. Similarly, embodimentsdescribed herein use drusen and RPE-disruption as example demonstrationand the same embodiments can be applied to retinal disruption generally.Although various embodiments that incorporate the teachings of thepresent invention have been illustrated and described in detail herein,a person of ordinary skill in the art can readily device other variousembodiments that incorporate the teachings of this subject invention.

We claim:
 1. An imaging apparatus, comprising: an optical source; an x-yscanner receiving light from the optical source and directing it onto asample; a detector receiving reflected light from the scanner; and acomputer receiving a signal from the detector and executing code storedin a memory, the code providing instructions for providing a 3D data setcontaining voxels of a sample, dentifying one or more 3D seeds from the3D data set by using an adaptive seed search image (ASSI), obtainingcharacteristics of 3D disruptions based on the 3D seeds, generatingmeasurements for the 3D disruptions, and displaying the results of the3D disruptions.
 2. The apparatus of claim 1, wherein identifying one ormore 3D seeds from the 3D data set includes: segmenting at least oneretinal layer in the 3D data set to obtain one or more segmented retinallayers; generating a notinal Retinal Pigment Epithelium (RPE) layer fromthe at least one retinal layer in the 3D data set to obtain a RPE fitsurface; and calculating a distance between points on the one or moresegmented retinal layers to points on the RPE fit surface to obtain atleast one elevation map.
 3. The apparatus of claim 2, further includingreducing the 3D data set between the one or more segmented retinallayers into a 2D representation to obtain an en face image.
 4. Theapparatus of claim 3, wherein the en face image is processed by applyingan image processing filter to obtain the ASSI, the processing filterbeing chosen from a set of filters consisting of an edge detectionfilter, a smoothing filter, and the combination thereof.
 5. Theapparatus of claim 3, wherein the ASSI can be generated by combininginformation of the elevation map and the en face image.
 6. The apparatusof claim 2, wherein identifying one or more 3D seeds from the 3D dataset includes a 3D segmentation computation.
 7. The apparatus of claim 6,wherein the 3D segmentation computation includes a 3D volumeconstruction method by interpolating two or more 2D cross-sectionalcontour images from the 3D dataset.
 8. The apparatus of claim 6, whereinthe 3D segmentation computation includes a 3D region growing methodusing region growing constraints.
 9. The apparatus of claim 1, whereinthe ASSI is an elevation map.
 10. The apparatus of claim 1, whereingenerating measurements for the 3D disruptions is interactive.
 11. Theapparatus of claim 1, wherein displaying the results of the 3Ddisruptions includes providing plots of one or more 3D disruptioncharacteristics in relation to at least one anatomical feature of aneye.
 12. The apparatus of claim 1, further including a progressionanalysis to evaluate and display 3D disruption results from the 3D dataset obtained from different points in time.
 13. The apparatus of claim1, further including a 3D display of 3D disruption clouds overlaying asegmented layer of interest in a 3D display interface.
 14. An imageprocessing method, comprising: acquiring a 3D data set containing voxelsof a sample from an Optical Coherent Tomography (OCT) system;identifying one or more 3D seeds from the 3D data set by using anadaptive seed search image (ASSI); obtaining characteristics of 3Ddisruptions based on the 3D seeds; generating measurements for the 3Ddisruptions; and displaying results of the 3D disruptions.
 15. Themethod of claim 14, wherein identifying one or more 3D seeds from the 3Ddata set includes: segmenting at least one retinal layer in the 3D dataset to obtain one or more segmented retinal layers; generating a normalRetinal Pigment Epithelium (RPE) layer from the at least one retinallayer in the 3D data set to obtain a RPE fit surface; and calculating adistance between points on the one or more segmented retinal layers topoints on the RPE fit surface to obtain at least one elevation map. 16.The method of claim 15, further including reducing the 3D data setbetween the one or more segmented retinal layers into a 2Drepresentation to obtain an en face image.
 17. The method of claim 16,wherein the en face image is processed by applying an image processingfilter to obtain the ASSI, the processing filter being chosen from a setof filters consisting of an edge detection filter, a smoothing filter,and the combination thereof.
 18. The method of claim 16, wherein theASSI can be generated by combining information of the elevation map andthe en face image.
 19. The method of claim 15, wherein identifying oneor more 3D seeds from the 3D data set includes a 3D segmentationcomputation.
 20. The method of claim 19, wherein the 3D segmentationcomputation includes a 3D volume construction method by interpolatingtwo or more 2D cross-sectional contour images from the 3D dataset. 21.The method of claim 19, wherein the 3D segmentation computation includesa 3D region growing method using region growing constraints.
 22. Themethod of claim 14, wherein the ASSI is an elevation map.
 23. The methodof claim 14, wherein generating measurements for the 3D disruptions isinteractive.
 24. The method of claim 14, wherein displaying the resultsof the 3D disruptions includes providing plots of one or more 3Ddisruption characteristics in relation to at least one anatomicalfeature of an eye.
 25. The method of claim 14, further including aprogression analysis to evaluate and display 3D disruption results fromthe 3D data set obtained from different points in time.
 26. The methodof claim 14, further including a 3D display of 3D disruption cloudsoverlaying a segmented layer of interest in a 3D display interface.